The Effect of Leisure Constraints and Leisure Facilitators on "Working-after-retirement" Older Adults' Leisure Participation
Bibliographic record
Abstract
Background: The aging population is becoming a global concern. Leisure can benefit older adults’ well-being, which can help older adults pursue successful aging. Older adults usually have more free time after their retirement. However, more and more older adults choose to work after retirement age. In Canada, older adults who are 60 years old or older can receive the retirement pension from the Canada Pension Plan (CPP), but one in five Canadian older adults aged over 65 years were still working in 2015 (Statistics Canada, 2017). This “working-after-retirement” (WAR) issue has not received attention within leisure studies. WAR is a complicated issue. It may bring both leisure constraints, such as lack of energy, and leisure facilitators, such as having coworkers as leisure companions. Objective: The purpose of my thesis was to investigate the relationship between leisure participation, leisure constraints, leisure facilitators, leisure motivation, and constraints negotiation among older adults who are experiencing WAR. Methods: An online survey was conducted to collect quantitative data regarding leisure participation, leisure constraints, leisure facilitators, constraints negotiation strategies, and leisure motivation from older adults in Canada who experienced WAR. The survey was distributed to Qualtrics’ panelists. A total of 417 participants completed the online survey. All participants were aged 55 years or older. After data cleaning, the final sample size was 396. Models were adopted from previous quantitative studies about leisure constraints and facilitators (Hubbard & Mannell, 2001; Son et al., 2008; Son et al., 2024). Partial least squares structural equation modeling (PLS-SEM) was applied to evaluate the validity of the measurement models and the structural model, examine the explanatory and predictive power of the models, and compare the models. Results: Eight modified constraints-negotiation models were tested in my thesis. The results indicated that leisure constraints were negatively related to leisure participation, while constraint negotiation and leisure motivation were positively related to leisure participation across all the models. I also found constraint negotiation partially mediated the paths between leisure motivation and leisure participation. The moderation effects of constraint negotiation and leisure constraints were not significant. All the models had weak explanatory power (.25 R2 < .50) based on Hair et al. (2011). The Mitigation and Moderation models had the highest PLS predictive power compared to linear regression model benchmark, while the Independence model was best among all the models in terms of BIC values. Discussion and Implications: My results supported the Independence and Mitigation models proposed by Hubbard and Mannell (2001). The Dual-channel and Facilitators models developed by Son et al. (2008) and Son et al. (2024) were supported as well. My results did not support the hypothesized moderation effect of constraint negotiation, which is in line with the lack of empirical support for the Buffer model (Hubbard & Mannell, 2001). In terms of model comparison, my findings suggested that the Independence model was the best model considering theoretical consistency and predictive power, although the Mitigation and Dual-channel models were favored in Hubbard and Mannell (2001) and Son et al. (2008), respectively. In terms of theoretical implications, my results favored the Independence model the most. The findings also supported my assumption that leisure facilitators play the role that parallels leisure constraints in the constraint negotiation process. With regard to practical implications, the most commonly reported leisure facilitators and negotiation strategies were intrapersonal facilitators (e.g., my leisure activities are enjoyable) and skill acquisition (e.g., I try to learn new leisure activities), respectively. Knowing these facilitators and negotiation strategies can encourage older adults to pursue leisure better. My study also suggested providing leisure education programs for WAR older adults may be a promising avenue to support rich leisure lives within this population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".